● Rottawhite — AI Systems Studio

The agent, the evals, and the audit trail — in production.

Most agent projects stall because nobody can prove the thing is right. We ship the proof alongside the system.

Price
Scoped on a call
Scoped on a call, against what the workflow costs you today.
Duration
6–10 weeks
Scope a build →See the workflows →

What you get

Inside the production ai agent build

The agent, running in your stack

Integrated with the systems the work already flows through — your agency management system, inbox, document store — with permissions and error handling that survive contact with real volume.

An eval harness

A labelled test set and a scoring run that gates every change. When a prompt or a model is updated, you find out whether accuracy moved before it reaches production rather than after.

A full audit trail

Every extraction traceable to the source document and page, every automated action logged with its inputs. This is what makes the system defensible when someone asks how a number was arrived at.

Human-in-the-loop where it matters

Confidence thresholds that route the uncertain cases to a person instead of guessing. The goal is to remove the routine ninety percent, not to pretend the last ten does not exist.

A runbook and handover

Documentation your team can operate from, plus thirty days of post-launch support. Full IP transfer on final payment.

How it runs

The shape of the engagement

Weeks 1–2
Workflow mapping, integration access, and the labelled test set that everything downstream is measured against.
Weeks 3–6
Build in two-week increments with a working demo at the end of each. Accuracy tracked against the test set throughout.
Weeks 7–8
Integration, exception handling, audit logging, and load testing against real volume.
Weeks 9–10
Parallel running alongside the manual process, tuning thresholds, then handover.

Fit

This is for you if

  • A workflow is already validated — by a sprint, a pilot, or painful experience
  • The output feeds a regulated or client-facing process, so being wrong has a cost
  • You need to show someone — a carrier, an auditor, a board — why the system can be trusted

It is not, if

  • You want a chatbot bolted onto a website
  • Nobody internally will own the system after handover
  • The requirement is still "we should do something with AI"

Questions

Before you book

Why does the eval harness matter so much?

Because without one you cannot answer the only question that matters: is it still right? Models change, prompts get edited, document formats drift. A system with no test set degrades silently, and in regulated work silent degradation is the expensive kind.

How is this priced?

On a call, against the cost of the workflow as it runs today — headcount, turnaround, and error rates. Published numbers tend to be either too high for a narrow workflow or too low for a system touching four integrations.

Which systems do you integrate with?

Agency management and policy administration systems, document stores, and email. Where there is an API we use it; where there is not, we work with exports and file drops rather than pretending the integration is clean.

What happens if accuracy is not good enough?

You find out during the build, from the test set, rather than in production. We would rather narrow the scope to the part that works than ship something nobody trusts.

The ladder

Where this sits

01 — $2,500

AI Integration Sprint

You should not have to sign a six-figure contract to find out whether an AI agent can read your submissions. Spend a week and a fixed fee instead.

03 — Scoped

AgentOps Retainer

Document formats change, carriers change their templates, models get deprecated. Something has to notice.

Next step

Start with one workflow.

A week, a fixed fee, and a measured answer on your own documents. If it will not work, you find out for $2,500.

Book a 30-min call →The $2,500 sprint →